Taking similar approaches, recent few-shot segmentation methods [1]}, [2]}, [3]}, [4]}, [5]} also attempted to leverage pixel-wise information by refining features either by using cross-attention [5]} or graph attention [1]}, [2]}, [3]}. However, as proven in semantic correspondence literature [10]}, [11]}, without aggregating the matching scores, and solely relying on raw correlation maps between features may suffer from the challenges posed due to ambiguities generated by repetitive patterns or background clutters [12]}, [13]}, [14]}, [15]}, [16]}. To address this, one of the latest work, HSNet [17]}, attempts to aggregate the matching scores with 4D convolutions, but it lacks an ability to consider an interaction among the matching scores due to the inherent nature of convolutions.
